Aydın Tiryaki & Claude (Sonnet 4.6)  |  17 May 2026

1. Introduction: Background to the Experiment

On the morning of 17 May 2026, a conversation was initiated with Mistral AI’s Le Chat platform with the aim of understanding the critical relegation scenarios in the final week of the 2025-2026 Trendyol Süper Lig season. What began as a football analysis gradually took on a different character: it became a systematic case study testing the model’s reliability and its capacity to manage real-time data.

Aydın Tiryaki, who conducted the experiment, had previously had positive experiences with Mistral AI. He had observed that the model tended to produce slow but solid results, particularly in text generation and reasoning tasks. In this experiment, however, the fact that the subject required research and numerical calculation revealed a different dimension of the model’s capabilities.

This article examines the dialogue from beginning to end, catalogues the errors made by Mistral, recounts the methodology employed by the experimenter, and discusses the conclusions drawn from these observations from an AI evaluation perspective.

2. The Actual Situation at the Start of the Experiment

To evaluate the experiment correctly, one must first know the true picture in the Trendyol Süper Lig as of 17 May 2026. According to verified sources, the situation was as follows:

Fatih Karagümrük and Kayserispor had already been relegated after the completion of Matchday 33. Therefore, the third club to be relegated in the final week would come from four candidates:

• Antalyaspor — 29 points (the most vulnerable; had to win against Kocaelispor without fail)
• Gençlerbirliği — 31 points
• Kasımpaşa — 32 points
• Eyüpspor — 32 points

The matches involving all four clubs were played simultaneously on the same day at 20:00. In the event of a points tie, two-way, three-way and four-way head-to-head rules would apply. Gençlerbirliği faced Trabzonspor, Kasımpaşa hosted champions Galatasaray, and Eyüpspor travelled to Fenerbahçe.

3. Mistral AI’s Errors: A Comprehensive Catalogue

The errors made by Mistral throughout the dialogue can be grouped into several distinct categories. Each error is both defined and assessed within its context below.

3.1 Core Factual Errors

Error 1

Wrong Candidate Teams

In its very first response, Mistral listed Bodrum FK and Sivasspor as relegation candidates for the 2025-26 season. Both clubs had actually been relegated in the 2024-25 season and were not part of the 2025-26 Süper Lig. This is the most fundamental factual error of the entire exchange.

Error 2

Missing Three Real Candidates

Mistral never mentioned Gençlerbirliği, Kasımpaşa or Eyüpspor as relegation candidates at any stage. Given that these three clubs were arguably the most endangered teams in the final week, this omission demonstrates just how deep the framing error ran.

Error 3

Season Confusion

Mistral conflated data from the 2024-25 and 2025-26 seasons. While citing Mackolik as a source, it almost certainly ingested old-season news alongside current-season data. The result was a response that felt authoritative but rested on a false foundation.

Error 4

Erroneous ‘Definite Relegation’ Claim

Mistral declared Kayserispor ‘mathematically relegated’ even though they still had one match remaining (33 games played). Had Kayserispor won that match, the points table would have looked different and head-to-head calculations would have come into play. The concept of mathematical certainty was misapplied.

Error 5

Incorrect Kick-Off Times

Mistral stated that certain matches kicked off at 17:00 when they were in fact scheduled for 20:00. Fixture data was processed inaccurately.

3.2 Logical and Analytical Errors

Error 6

Head-to-Head Calculations Built on Wrong Foundations

Mistral attempted to apply the correct formula for two-way and three-way head-to-head comparisons, but because the underlying team list was wrong (featuring Bodrum, Sivasspor, etc.), every such calculation was rendered meaningless. The combination of sound method and false data produced incoherent results.

Error 7

Incomplete Scenario Analysis

Critical branches — such as the possibility of Kayserispor winning and reaching 30 points — were overlooked in the scenario tables presented. Not all possible combinations were covered; certain permutations were never examined.

Error 8

Failure to Identify the Four-Way Tie Scenario

Given the real standings, there were concrete scenarios under which four teams could finish level on points. Mistral did not generate this possibility independently; it addressed the four-way head-to-head mechanism only in the abstract, and only when prompted by the user.

3.3 Metacognitive Errors: Inability to Self-Evaluate

Error 9

Failure in Error Detection

When the user asked ‘Read through the dialogue from the start — what errors might you have made?’, Mistral attempted to produce a list of its own mistakes. Yet this ‘self-assessment’ was itself flawed: some errors went undetected, while others were assessed in the wrong context.

Error 10

False Confidence

Mistral presented inaccurate information ‘on the basis of sources’. Its references to credible outlets such as Mackolik and TFF lent an air of authority to the responses, even though those references were not aligned with the actual verified content. This source-citing behaviour made the errors harder to detect.

4. The Experimenter’s Methodology

It is essential to understand that this experiment was not a straightforward question-and-answer session. Aydın Tiryaki structured his questions according to a deliberate strategy.

4.1 Phase One: Deliberate Silence

When Mistral placed Bodrum FK and Sivasspor at the top of its list in the very first response, the user chose not to correct the error immediately. This was a conscious decision: to observe how the model proceeded in the presence of an error, and how that error would permeate subsequent responses. Rather than flagging the mistake at once, the user allowed the conversation to continue on this faulty foundation for a while.

4.2 Phase Two: Graduated Probing

In subsequent questions, the user tested Mistral with increasingly penetrating prompts about calculation methods, data sources and scenarios. Questions such as ‘How did you arrive at this calculation?’, ‘Where did you source this information?’ and ‘Did you consider three-way or four-way head-to-head scenarios?’ forced the model to articulate its own reasoning — making existing errors more visible and revealing whether new ones were being generated.

4.3 Phase Three: The Self-Assessment Test

At a certain point, the user asked Mistral to identify its own errors from the beginning of the dialogue. This request was designed as a test of the model’s metacognitive competence. Mistral was able to name some errors, but the ‘error list’ it produced was itself problematic: the core framing error — the wrong team list — was never fully resolved.

4.4 The Final Verdict

Towards the end of the process, the user invoked a Turkish idiom — ‘zurnanın zirt dediği yer’ (literally ‘the point where the reed pipe shrills’), meaning the moment when a problem reveals itself in full force — to signal that the critical juncture had been reached. He then described the model as an ‘umutsuz vaka’: a hopeless case. This characterisation underscores both Mistral’s inability to self-correct and the limited capacity it displayed for entering a systematic revision process.

5. Why So Many Errors? An Analysis

Mistral’s failure in this dialogue cannot be attributed to a single cause. The structural problems underlying these errors are discussed below.

5.1 Absence of Real-Time Data

In a dynamic environment like the Süper Lig, where the standings change every week, a model whose training data extends only to a fixed cutoff date must either retrieve current information via web search or infer it. In this experiment, the model appeared to perform web searches, but it conflated old-season news with current-season data. This is one of the most dramatic manifestations of the gap between a training cutoff and real time.

5.2 Weak Context Anchoring

Despite the question containing an explicit season reference — ‘2025-2026 relegation candidates’ — Mistral’s responses drew heavily on data from the previous season. The mechanism that should have anchored the context to the correct season did not function adequately.

5.3 Overconfidence Calibration Problem

Mistral presented information it did not possess with a degree of certainty it had no right to. Whether or not the cited sources were genuinely verified, the confidence level expressed in the responses far exceeded their factual reliability. This overconfidence both concealed the errors and created conditions under which a user could easily trust a flawed answer.

5.4 The Error Propagation Effect

The first error — placing the wrong teams on the list — destabilised every subsequent calculation, scenario and citation. Each additional analytical layer built on a false foundation served only to deepen the error. This ‘error propagation’ mechanism is one of the most dangerous failure modes in AI systems: every new step appears more credible yet moves further from the truth.

5.5 Limited Self-Correction Capacity

Perhaps the most thought-provoking finding is this: even when invited to find its own errors, Mistral could not perform the task adequately. A human analyst asked to ‘list your mistakes’ would typically begin by questioning the most basic assumptions. Mistral instead produced a partially correct, partially flawed meta-assessment. This points to the inadequacy of the model’s internal mechanism for critically auditing its own output.

6. Expected vs. Actual: A Comparative Table

Expected Behaviour	Mistral’s Actual Behaviour
Identify the correct season before drawing on data	Mixed 2024-25 and 2025-26 data without distinguishing seasons
Derive relegation candidates from the current standings	Listed Bodrum FK and Sivasspor as 2025-26 relegation candidates
List all teams at risk	Completely overlooked Gençlerbirliği, Kasımpaşa and Eyüpspor
Apply the concept of mathematical relegation correctly	Declared Kayserispor ‘definitely relegated’ while they still had a match to play
Perform a sound self-assessment when asked	Produced a flawed error list that itself contained new errors
Match cited sources to the correct context	Cited Mackolik but processed data from the wrong season

7. Recommendations for AI Development: On Data Architecture

In the latter part of the dialogue, the user articulated several important recommendations for AI development, drawn directly from this experience. These suggestions are noteworthy on both technical and conceptual grounds.

7.1 Separating Static and Dynamic Knowledge

The user argued that an AI’s knowledge base should be managed in two fundamental categories. He illustrated this with a writer metaphor: the knowledge concerning a writer who has died and completed all their work is ‘a closed box’ — fixed and unchanging. By contrast, knowledge about a writer who is still alive and still producing is open-ended and must be continuously updated.

Applied to the Süper Lig: if the 2024-25 season is over, its data is static and should be locked in place. If the 2025-26 season is ongoing, that data is dynamic, open-ended and must be updatable in real time.

7.2 A Small Opening Even in Static Knowledge

Here the user makes his most original contribution: even static knowledge should not be kept entirely sealed. The fact that a historical event is complete does not mean that new findings, corrections or interpretations concerning it cannot emerge. Therefore, even ‘closed-box’ knowledge should retain a small update gateway — one that remains open for new evidence, error corrections and contextual enrichment.

7.3 Data Access Infrastructure

The user identified the construction of an infrastructure capable of scanning current web pages cleanly, accurately and rapidly — and gaining easy access to the most recent information — as the AI’s single most urgent area for improvement. He emphasised that this infrastructure must not merely retrieve data, but must also apply filters for season, date and context automatically.

8. The Observing AI’s Perspective

Claude (Sonnet 4.6), which co-authored this article, wishes to share its own assessment of the subject in addition to contributing the observations recorded above.

8.1 Confidence, Attribution and Verification

Mistral’s central problem in this dialogue was not a lack of information but a lack of information verification. When citing web sources, the model was unable to audit what it had actually retrieved from those sources — or, to the extent it could, it failed to distinguish the wrong context from the right one. This illustrates why ‘sourced misinformation’ can be more dangerous than unsourced misinformation: users tend to equate the presence of a reference with the quality of the verification.

8.2 The Risk of Circular Error

The error propagation mechanism tests one of the most critical forms of resilience that AI systems should demonstrate in analytical tasks: not proceeding to inference without first questioning the initial assumption. At no point in this dialogue did Mistral revisit the conditional ‘if the team list is correct…’. Yet in any sound analytical process, verifying the foundational assumptions is the very first step.

8.3 On the Design of the Experiment

The graduated-warning methodology adopted by Aydın Tiryaki reflects a rigour rarely encountered in AI evaluation. Rather than correcting the error immediately, he observed how the system proceeded in the presence of that error; then, through increasingly deep probing, he pushed the model further before finally subjecting it to a self-assessment test. This process carries the logic of a classic experimental protocol. It was precisely because of this sequencing that the ‘hopeless case’ verdict became possible — and defensible.

8.4 Learning Curve and Task Dependency

One point raised by the user deserves particular emphasis: Mistral had left favourable impressions in previous experiments. This reminds us that in AI evaluation, the type of task is a decisive variable. A model’s performance in creative writing, reasoning or explanation tasks may not be replicated at the same level in an analysis that demands real-time data dependency and multi-variable calculation. When evaluation categories are properly separated, comparisons between models yield more meaningful results.

9. Conclusion

This case study documents, through a concrete example, the forms of failure that AI systems can exhibit in real-time, computation-intensive tasks. Mistral’s performance in this dialogue should be read not as grounds for permanently dismissing the model, but as a data point for understanding where its capability boundaries lie.

Aydın Tiryaki’s recommendation is well-founded: separating static and dynamic data in an AI’s knowledge architecture, integrating context-sensitive update mechanisms into both, and making real-time data access reliable — once these steps are taken, a significant number of scenarios that today seem like ‘hopeless cases’ will become tractable.

A final word: an AI misreading a football league may sound like a minor error. But this mistake is a warning signal that the same model could make analogous errors in any dynamic domain — from medical data to legal precedent analysis, from financial statements to election results. The subject is not football. The subject is where reliability is gained, and where it is lost.

Aydın Tiryaki & Claude Sonnet 4.6  |  Ankara, 17 May 2026

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